Overview

Dataset statistics

Number of variables20
Number of observations90275
Missing cells894694
Missing cells (%)49.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.5 MiB
Average record size in memory191.4 B

Variable types

Numeric16
DateTime1
Categorical3

Alerts

buildingclasstypeid has constant value ""Constant
decktypeid has constant value ""Constant
airconditioningtypeid is highly overall correlated with fipsHigh correlation
architecturalstyletypeid is highly overall correlated with fipsHigh correlation
basementsqft is highly overall correlated with bedroomcnt and 1 other fieldsHigh correlation
bathroomcnt is highly overall correlated with bedroomcnt and 5 other fieldsHigh correlation
bedroomcnt is highly overall correlated with basementsqft and 7 other fieldsHigh correlation
buildingqualitytypeid is highly overall correlated with fipsHigh correlation
calculatedbathnbr is highly overall correlated with bathroomcnt and 6 other fieldsHigh correlation
finishedfloor1squarefeet is highly overall correlated with bedroomcnt and 4 other fieldsHigh correlation
calculatedfinishedsquarefeet is highly overall correlated with bathroomcnt and 8 other fieldsHigh correlation
finishedsquarefeet12 is highly overall correlated with bathroomcnt and 5 other fieldsHigh correlation
finishedsquarefeet13 is highly overall correlated with calculatedbathnbr and 2 other fieldsHigh correlation
finishedsquarefeet15 is highly overall correlated with bathroomcnt and 3 other fieldsHigh correlation
finishedsquarefeet50 is highly overall correlated with finishedfloor1squarefeet and 3 other fieldsHigh correlation
finishedsquarefeet6 is highly overall correlated with bathroomcnt and 4 other fieldsHigh correlation
fips is highly overall correlated with airconditioningtypeid and 7 other fieldsHigh correlation
airconditioningtypeid has 61494 (68.1%) missing valuesMissing
architecturalstyletypeid has 90014 (99.7%) missing valuesMissing
basementsqft has 90232 (> 99.9%) missing valuesMissing
buildingclasstypeid has 90259 (> 99.9%) missing valuesMissing
buildingqualitytypeid has 32911 (36.5%) missing valuesMissing
calculatedbathnbr has 1182 (1.3%) missing valuesMissing
decktypeid has 89617 (99.3%) missing valuesMissing
finishedfloor1squarefeet has 83419 (92.4%) missing valuesMissing
finishedsquarefeet12 has 4679 (5.2%) missing valuesMissing
finishedsquarefeet13 has 90242 (> 99.9%) missing valuesMissing
finishedsquarefeet15 has 86711 (96.1%) missing valuesMissing
finishedsquarefeet50 has 83419 (92.4%) missing valuesMissing
finishedsquarefeet6 has 89854 (99.5%) missing valuesMissing
parcelid is highly skewed (γ1 = 29.94874413)Skewed
bathroomcnt has 1165 (1.3%) zerosZeros
bedroomcnt has 1421 (1.6%) zerosZeros

Reproduction

Analysis started2023-11-23 05:15:16.222887
Analysis finished2023-11-23 05:15:31.106921
Duration14.88 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

parcelid
Real number (ℝ)

SKEWED 

Distinct90150
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12984656
Minimum10711738
Maximum1.6296084 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:31.181023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum10711738
5-th percentile10858991
Q111559500
median12547337
Q314227552
95-th percentile17152676
Maximum1.6296084 × 108
Range1.522491 × 108
Interquartile range (IQR)2668052

Descriptive statistics

Standard deviation2504510.5
Coefficient of variation (CV)0.19288231
Kurtosis1742.7841
Mean12984656
Median Absolute Deviation (MAD)1343404
Skewness29.948744
Sum1.1721898 × 1012
Variance6.2725728 × 1012
MonotonicityNot monotonic
2023-11-23T00:15:31.259048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11842707 3
 
< 0.1%
11633771 2
 
< 0.1%
11742566 2
 
< 0.1%
14322378 2
 
< 0.1%
12097956 2
 
< 0.1%
11554091 2
 
< 0.1%
13037293 2
 
< 0.1%
12448490 2
 
< 0.1%
11735136 2
 
< 0.1%
10961914 2
 
< 0.1%
Other values (90140) 90254
> 99.9%
ValueCountFrequency (%)
10711738 1
< 0.1%
10711755 1
< 0.1%
10711805 1
< 0.1%
10711816 1
< 0.1%
10711858 1
< 0.1%
10711910 1
< 0.1%
10712086 1
< 0.1%
10712162 1
< 0.1%
10712163 1
< 0.1%
10712195 1
< 0.1%
ValueCountFrequency (%)
162960842 1
< 0.1%
162960829 1
< 0.1%
162960801 1
< 0.1%
162960769 1
< 0.1%
162960704 1
< 0.1%
162960682 1
< 0.1%
162960629 1
< 0.1%
162960603 1
< 0.1%
162960499 1
< 0.1%
162960482 1
< 0.1%

logerror
Real number (ℝ)

Distinct1894
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01145722
Minimum-4.605
Maximum4.737
Zeros847
Zeros (%)0.9%
Negative39667
Negative (%)43.9%
Memory size3.4 MiB
2023-11-23T00:15:31.424901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-4.605
5-th percentile-0.1267
Q1-0.0253
median0.006
Q30.0392
95-th percentile0.1647
Maximum4.737
Range9.342
Interquartile range (IQR)0.0645

Descriptive statistics

Standard deviation0.16107884
Coefficient of variation (CV)14.059156
Kurtosis131.37539
Mean0.01145722
Median Absolute Deviation (MAD)0.0323
Skewness2.1688283
Sum1034.3005
Variance0.025946391
MonotonicityNot monotonic
2023-11-23T00:15:31.495990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003 938
 
1.0%
0.002 915
 
1.0%
0.005 911
 
1.0%
0.001 901
 
1.0%
0.007 878
 
1.0%
0.004 869
 
1.0%
0.01 856
 
0.9%
0.006 853
 
0.9%
-0.005 853
 
0.9%
0 847
 
0.9%
Other values (1884) 81454
90.2%
ValueCountFrequency (%)
-4.605 2
< 0.1%
-4.51 1
 
< 0.1%
-3.194 1
 
< 0.1%
-2.976 1
 
< 0.1%
-2.688 1
 
< 0.1%
-2.397 1
 
< 0.1%
-2.375 1
 
< 0.1%
-2.365 2
< 0.1%
-2.354 3
< 0.1%
-2.333 2
< 0.1%
ValueCountFrequency (%)
4.737 1
< 0.1%
4.52 1
< 0.1%
4.445 1
< 0.1%
3.968 1
< 0.1%
3.443 1
< 0.1%
3.436 1
< 0.1%
3.403 1
< 0.1%
3.289 1
< 0.1%
3.25 1
< 0.1%
3.174 1
< 0.1%
Distinct352
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Minimum2016-01-01 00:00:00
Maximum2016-12-30 00:00:00
2023-11-23T00:15:31.561834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:31.631869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

airconditioningtypeid
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing61494
Missing (%)68.1%
Infinite0
Infinite (%)0.0%
Mean1.8163719
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:31.688072image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile13
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.9741679
Coefficient of variation (CV)1.6374223
Kurtosis9.9509234
Mean1.8163719
Median Absolute Deviation (MAD)0
Skewness3.4414602
Sum52277
Variance8.845675
MonotonicityNot monotonic
2023-11-23T00:15:31.745097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 26668
29.5%
13 1833
 
2.0%
5 215
 
0.2%
11 63
 
0.1%
9 1
 
< 0.1%
3 1
 
< 0.1%
(Missing) 61494
68.1%
ValueCountFrequency (%)
1 26668
29.5%
3 1
 
< 0.1%
5 215
 
0.2%
9 1
 
< 0.1%
11 63
 
0.1%
13 1833
 
2.0%
ValueCountFrequency (%)
13 1833
 
2.0%
11 63
 
0.1%
9 1
 
< 0.1%
5 215
 
0.2%
3 1
 
< 0.1%
1 26668
29.5%

architecturalstyletypeid
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)2.3%
Missing90014
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean7.2298851
Minimum2
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:31.794893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q17
median7
Q37
95-th percentile8
Maximum21
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7161957
Coefficient of variation (CV)0.37569002
Kurtosis18.456565
Mean7.2298851
Median Absolute Deviation (MAD)0
Skewness3.6821707
Sum1887
Variance7.3777188
MonotonicityNot monotonic
2023-11-23T00:15:31.847843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
7 221
 
0.2%
8 16
 
< 0.1%
2 11
 
< 0.1%
21 8
 
< 0.1%
3 4
 
< 0.1%
10 1
 
< 0.1%
(Missing) 90014
99.7%
ValueCountFrequency (%)
2 11
 
< 0.1%
3 4
 
< 0.1%
7 221
0.2%
8 16
 
< 0.1%
10 1
 
< 0.1%
21 8
 
< 0.1%
ValueCountFrequency (%)
21 8
 
< 0.1%
10 1
 
< 0.1%
8 16
 
< 0.1%
7 221
0.2%
3 4
 
< 0.1%
2 11
 
< 0.1%

basementsqft
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)90.7%
Missing90232
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean713.5814
Minimum100
Maximum1555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:31.908725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile169.6
Q1407.5
median616
Q3872
95-th percentile1528
Maximum1555
Range1455
Interquartile range (IQR)464.5

Descriptive statistics

Standard deviation437.4342
Coefficient of variation (CV)0.61301234
Kurtosis-0.55778761
Mean713.5814
Median Absolute Deviation (MAD)286
Skewness0.6518958
Sum30684
Variance191348.68
MonotonicityNot monotonic
2023-11-23T00:15:31.975782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1528 3
 
< 0.1%
700 2
 
< 0.1%
1312 2
 
< 0.1%
515 1
 
< 0.1%
162 1
 
< 0.1%
616 1
 
< 0.1%
1551 1
 
< 0.1%
485 1
 
< 0.1%
238 1
 
< 0.1%
493 1
 
< 0.1%
Other values (29) 29
 
< 0.1%
(Missing) 90232
> 99.9%
ValueCountFrequency (%)
100 1
< 0.1%
162 1
< 0.1%
168 1
< 0.1%
184 1
< 0.1%
196 1
< 0.1%
198 1
< 0.1%
234 1
< 0.1%
238 1
< 0.1%
260 1
< 0.1%
312 1
< 0.1%
ValueCountFrequency (%)
1555 1
 
< 0.1%
1551 1
 
< 0.1%
1528 3
< 0.1%
1350 1
 
< 0.1%
1312 2
< 0.1%
1210 1
 
< 0.1%
1048 1
 
< 0.1%
913 1
 
< 0.1%
831 1
 
< 0.1%
814 1
 
< 0.1%

bathroomcnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2794738
Minimum0
Maximum20
Zeros1165
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:32.038314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.004271
Coefficient of variation (CV)0.4405714
Kurtosis5.9804844
Mean2.2794738
Median Absolute Deviation (MAD)0.5
Skewness1.289638
Sum205779.5
Variance1.0085602
MonotonicityNot monotonic
2023-11-23T00:15:32.102015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 36534
40.5%
3 19946
22.1%
1 15035
16.7%
2.5 8091
 
9.0%
4 3743
 
4.1%
1.5 1641
 
1.8%
0 1165
 
1.3%
5 1142
 
1.3%
3.5 1091
 
1.2%
4.5 795
 
0.9%
Other values (13) 1092
 
1.2%
ValueCountFrequency (%)
0 1165
 
1.3%
1 15035
16.7%
1.5 1641
 
1.8%
2 36534
40.5%
2.5 8091
 
9.0%
3 19946
22.1%
3.5 1091
 
1.2%
4 3743
 
4.1%
4.5 795
 
0.9%
5 1142
 
1.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
15 1
 
< 0.1%
12 3
 
< 0.1%
11 3
 
< 0.1%
10 14
 
< 0.1%
9 36
 
< 0.1%
8.5 1
 
< 0.1%
8 114
0.1%
7.5 9
 
< 0.1%
7 155
0.2%

bedroomcnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0318693
Minimum0
Maximum16
Zeros1421
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:32.161974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum16
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1564355
Coefficient of variation (CV)0.38142658
Kurtosis4.3912379
Mean3.0318693
Median Absolute Deviation (MAD)1
Skewness0.76117327
Sum273702
Variance1.3373431
MonotonicityNot monotonic
2023-11-23T00:15:32.223614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3 35447
39.3%
2 22357
24.8%
4 20279
22.5%
5 5077
 
5.6%
1 3897
 
4.3%
0 1421
 
1.6%
6 1120
 
1.2%
8 274
 
0.3%
7 234
 
0.3%
9 91
 
0.1%
Other values (7) 78
 
0.1%
ValueCountFrequency (%)
0 1421
 
1.6%
1 3897
 
4.3%
2 22357
24.8%
3 35447
39.3%
4 20279
22.5%
5 5077
 
5.6%
6 1120
 
1.2%
7 234
 
0.3%
8 274
 
0.3%
9 91
 
0.1%
ValueCountFrequency (%)
16 4
 
< 0.1%
15 1
 
< 0.1%
14 3
 
< 0.1%
13 1
 
< 0.1%
12 22
 
< 0.1%
11 12
 
< 0.1%
10 35
 
< 0.1%
9 91
 
0.1%
8 274
0.3%
7 234
0.3%

buildingclasstypeid
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)6.2%
Missing90259
Missing (%)> 99.9%
Memory size3.4 MiB
4.0
16 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters48
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 16
 
< 0.1%
(Missing) 90259
> 99.9%

Length

2023-11-23T00:15:32.288900image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-23T00:15:32.337881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
4.0 16
100.0%

Most occurring characters

ValueCountFrequency (%)
4 16
33.3%
. 16
33.3%
0 16
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32
66.7%
Other Punctuation 16
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 16
50.0%
0 16
50.0%
Other Punctuation
ValueCountFrequency (%)
. 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 16
33.3%
. 16
33.3%
0 16
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 16
33.3%
. 16
33.3%
0 16
33.3%

buildingqualitytypeid
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing32911
Missing (%)36.5%
Infinite0
Infinite (%)0.0%
Mean5.5654069
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:32.383237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median7
Q37
95-th percentile7
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9006016
Coefficient of variation (CV)0.34150273
Kurtosis-0.089246812
Mean5.5654069
Median Absolute Deviation (MAD)0
Skewness-0.24259547
Sum319254
Variance3.6122866
MonotonicityNot monotonic
2023-11-23T00:15:32.436572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7 29310
32.5%
4 23839
26.4%
1 2627
 
2.9%
10 1461
 
1.6%
12 119
 
0.1%
8 5
 
< 0.1%
6 2
 
< 0.1%
11 1
 
< 0.1%
(Missing) 32911
36.5%
ValueCountFrequency (%)
1 2627
 
2.9%
4 23839
26.4%
6 2
 
< 0.1%
7 29310
32.5%
8 5
 
< 0.1%
10 1461
 
1.6%
11 1
 
< 0.1%
12 119
 
0.1%
ValueCountFrequency (%)
12 119
 
0.1%
11 1
 
< 0.1%
10 1461
 
1.6%
8 5
 
< 0.1%
7 29310
32.5%
6 2
 
< 0.1%
4 23839
26.4%
1 2627
 
2.9%

calculatedbathnbr
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)< 0.1%
Missing1182
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean2.3092162
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:32.491765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum20
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9761725
Coefficient of variation (CV)0.42272893
Kurtosis6.615842
Mean2.3092162
Median Absolute Deviation (MAD)0.5
Skewness1.497634
Sum205735
Variance0.95291275
MonotonicityNot monotonic
2023-11-23T00:15:32.551196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2 36534
40.5%
3 19946
22.1%
1 15035
16.7%
2.5 8084
 
9.0%
4 3743
 
4.1%
1.5 1636
 
1.8%
5 1142
 
1.3%
3.5 1088
 
1.2%
4.5 793
 
0.9%
6 448
 
0.5%
Other values (12) 644
 
0.7%
(Missing) 1182
 
1.3%
ValueCountFrequency (%)
1 15035
16.7%
1.5 1636
 
1.8%
2 36534
40.5%
2.5 8084
 
9.0%
3 19946
22.1%
3.5 1088
 
1.2%
4 3743
 
4.1%
4.5 793
 
0.9%
5 1142
 
1.3%
5.5 253
 
0.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
15 1
 
< 0.1%
12 3
 
< 0.1%
11 3
 
< 0.1%
10 14
 
< 0.1%
9 36
 
< 0.1%
8.5 1
 
< 0.1%
8 114
0.1%
7.5 9
 
< 0.1%
7 155
0.2%

decktypeid
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing89617
Missing (%)99.3%
Memory size3.4 MiB
66.0
658 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters2632
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row66.0
2nd row66.0
3rd row66.0
4th row66.0
5th row66.0

Common Values

ValueCountFrequency (%)
66.0 658
 
0.7%
(Missing) 89617
99.3%

Length

2023-11-23T00:15:32.614840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-23T00:15:32.662317image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
66.0 658
100.0%

Most occurring characters

ValueCountFrequency (%)
6 1316
50.0%
. 658
25.0%
0 658
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1974
75.0%
Other Punctuation 658
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 1316
66.7%
0 658
33.3%
Other Punctuation
ValueCountFrequency (%)
. 658
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2632
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 1316
50.0%
. 658
25.0%
0 658
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 1316
50.0%
. 658
25.0%
0 658
25.0%

finishedfloor1squarefeet
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1886
Distinct (%)27.5%
Missing83419
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean1347.974
Minimum44
Maximum7625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:32.720303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile563
Q1938
median1244
Q31614
95-th percentile2464.5
Maximum7625
Range7581
Interquartile range (IQR)676

Descriptive statistics

Standard deviation652.39903
Coefficient of variation (CV)0.48398486
Kurtosis8.4884824
Mean1347.974
Median Absolute Deviation (MAD)335.5
Skewness1.9824074
Sum9241710
Variance425624.49
MonotonicityNot monotonic
2023-11-23T00:15:32.795892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1226 24
 
< 0.1%
1260 24
 
< 0.1%
1431 24
 
< 0.1%
817 24
 
< 0.1%
1612 21
 
< 0.1%
1104 21
 
< 0.1%
798 20
 
< 0.1%
880 20
 
< 0.1%
960 19
 
< 0.1%
1233 19
 
< 0.1%
Other values (1876) 6640
 
7.4%
(Missing) 83419
92.4%
ValueCountFrequency (%)
44 1
 
< 0.1%
47 1
 
< 0.1%
49 4
< 0.1%
61 4
< 0.1%
63 3
< 0.1%
64 3
< 0.1%
66 1
 
< 0.1%
69 5
< 0.1%
79 6
< 0.1%
96 1
 
< 0.1%
ValueCountFrequency (%)
7625 1
< 0.1%
6870 1
< 0.1%
6805 1
< 0.1%
6615 1
< 0.1%
6572 1
< 0.1%
6271 1
< 0.1%
5701 1
< 0.1%
5416 1
< 0.1%
5273 1
< 0.1%
5264 1
< 0.1%

calculatedfinishedsquarefeet
Real number (ℝ)

HIGH CORRELATION 

Distinct5102
Distinct (%)5.7%
Missing661
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean1773.186
Minimum2
Maximum22741
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:32.869649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile819
Q11184
median1540
Q32095
95-th percentile3501
Maximum22741
Range22739
Interquartile range (IQR)911

Descriptive statistics

Standard deviation928.16239
Coefficient of variation (CV)0.52344334
Kurtosis18.86517
Mean1773.186
Median Absolute Deviation (MAD)418
Skewness2.7662235
Sum1.5890229 × 108
Variance861485.43
MonotonicityNot monotonic
2023-11-23T00:15:32.938860image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 188
 
0.2%
1080 179
 
0.2%
1440 157
 
0.2%
1120 155
 
0.2%
1040 151
 
0.2%
1280 148
 
0.2%
1300 145
 
0.2%
1260 142
 
0.2%
1400 142
 
0.2%
960 140
 
0.2%
Other values (5092) 88067
97.6%
(Missing) 661
 
0.7%
ValueCountFrequency (%)
2 1
< 0.1%
40 1
< 0.1%
66 1
< 0.1%
120 2
< 0.1%
152 1
< 0.1%
160 1
< 0.1%
199 1
< 0.1%
200 1
< 0.1%
214 1
< 0.1%
230 1
< 0.1%
ValueCountFrequency (%)
22741 1
< 0.1%
20013 1
< 0.1%
18577 1
< 0.1%
16814 1
< 0.1%
15973 1
< 0.1%
14870 1
< 0.1%
14699 1
< 0.1%
14484 1
< 0.1%
13433 1
< 0.1%
13377 1
< 0.1%

finishedsquarefeet12
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4982
Distinct (%)5.8%
Missing4679
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean1745.4545
Minimum2
Maximum20013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:33.007619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile814
Q11172
median1518
Q32056
95-th percentile3416
Maximum20013
Range20011
Interquartile range (IQR)884

Descriptive statistics

Standard deviation909.94117
Coefficient of variation (CV)0.52132046
Kurtosis18.009686
Mean1745.4545
Median Absolute Deviation (MAD)406
Skewness2.798821
Sum1.4940393 × 108
Variance827992.93
MonotonicityNot monotonic
2023-11-23T00:15:33.084525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 184
 
0.2%
1080 176
 
0.2%
1120 152
 
0.2%
1040 149
 
0.2%
1280 146
 
0.2%
1300 144
 
0.2%
1260 140
 
0.2%
1400 139
 
0.2%
960 136
 
0.2%
1320 135
 
0.1%
Other values (4972) 84095
93.2%
(Missing) 4679
 
5.2%
ValueCountFrequency (%)
2 1
< 0.1%
40 1
< 0.1%
66 1
< 0.1%
120 2
< 0.1%
152 1
< 0.1%
160 1
< 0.1%
199 1
< 0.1%
200 1
< 0.1%
214 1
< 0.1%
230 1
< 0.1%
ValueCountFrequency (%)
20013 1
< 0.1%
18577 1
< 0.1%
16814 1
< 0.1%
15973 1
< 0.1%
14870 1
< 0.1%
14699 1
< 0.1%
14484 1
< 0.1%
13433 1
< 0.1%
13377 1
< 0.1%
13352 1
< 0.1%

finishedsquarefeet13
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)33.3%
Missing90242
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1404.5455
Minimum1056
Maximum1584
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:33.148776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1056
5-th percentile1209.6
Q11392
median1440
Q31440
95-th percentile1548
Maximum1584
Range528
Interquartile range (IQR)48

Descriptive statistics

Standard deviation110.10821
Coefficient of variation (CV)0.078394196
Kurtosis2.6016785
Mean1404.5455
Median Absolute Deviation (MAD)0
Skewness-1.3737846
Sum46350
Variance12123.818
MonotonicityNot monotonic
2023-11-23T00:15:33.205929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1440 17
 
< 0.1%
1344 3
 
< 0.1%
1248 3
 
< 0.1%
1392 2
 
< 0.1%
1536 2
 
< 0.1%
1566 1
 
< 0.1%
1464 1
 
< 0.1%
1152 1
 
< 0.1%
1584 1
 
< 0.1%
1416 1
 
< 0.1%
(Missing) 90242
> 99.9%
ValueCountFrequency (%)
1056 1
 
< 0.1%
1152 1
 
< 0.1%
1248 3
 
< 0.1%
1344 3
 
< 0.1%
1392 2
 
< 0.1%
1416 1
 
< 0.1%
1440 17
< 0.1%
1464 1
 
< 0.1%
1536 2
 
< 0.1%
1566 1
 
< 0.1%
ValueCountFrequency (%)
1584 1
 
< 0.1%
1566 1
 
< 0.1%
1536 2
 
< 0.1%
1464 1
 
< 0.1%
1440 17
< 0.1%
1416 1
 
< 0.1%
1392 2
 
< 0.1%
1344 3
 
< 0.1%
1248 3
 
< 0.1%
1152 1
 
< 0.1%

finishedsquarefeet15
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1915
Distinct (%)53.7%
Missing86711
Missing (%)96.1%
Infinite0
Infinite (%)0.0%
Mean2380.0901
Minimum560
Maximum22741
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:33.271584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum560
5-th percentile1200
Q11648
median2104.5
Q32862
95-th percentile4354.25
Maximum22741
Range22181
Interquartile range (IQR)1214

Descriptive statistics

Standard deviation1068.2072
Coefficient of variation (CV)0.44880956
Kurtosis38.534056
Mean2380.0901
Median Absolute Deviation (MAD)539.5
Skewness3.1035307
Sum8482641
Variance1141066.6
MonotonicityNot monotonic
2023-11-23T00:15:33.422675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1680 15
 
< 0.1%
1536 12
 
< 0.1%
1364 11
 
< 0.1%
1800 10
 
< 0.1%
1488 10
 
< 0.1%
1936 10
 
< 0.1%
2400 9
 
< 0.1%
2160 9
 
< 0.1%
1600 9
 
< 0.1%
1998 9
 
< 0.1%
Other values (1905) 3460
 
3.8%
(Missing) 86711
96.1%
ValueCountFrequency (%)
560 1
< 0.1%
609 1
< 0.1%
646 1
< 0.1%
700 1
< 0.1%
716 1
< 0.1%
759 1
< 0.1%
776 1
< 0.1%
792 1
< 0.1%
798 1
< 0.1%
847 1
< 0.1%
ValueCountFrequency (%)
22741 1
< 0.1%
8558 1
< 0.1%
8382 1
< 0.1%
7444 1
< 0.1%
7322 1
< 0.1%
7230 1
< 0.1%
7150 1
< 0.1%
6998 1
< 0.1%
6820 1
< 0.1%
6709 1
< 0.1%

finishedsquarefeet50
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1898
Distinct (%)27.7%
Missing83419
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean1355.5467
Minimum44
Maximum8352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:33.491869image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile563
Q1938
median1248
Q31619
95-th percentile2489.25
Maximum8352
Range8308
Interquartile range (IQR)681

Descriptive statistics

Standard deviation673.70349
Coefficient of variation (CV)0.49699763
Kurtosis11.686226
Mean1355.5467
Median Absolute Deviation (MAD)339
Skewness2.2855745
Sum9293628
Variance453876.39
MonotonicityNot monotonic
2023-11-23T00:15:33.562974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1226 24
 
< 0.1%
1431 24
 
< 0.1%
1260 24
 
< 0.1%
817 24
 
< 0.1%
1104 21
 
< 0.1%
1612 21
 
< 0.1%
798 20
 
< 0.1%
880 20
 
< 0.1%
734 19
 
< 0.1%
1233 19
 
< 0.1%
Other values (1888) 6640
 
7.4%
(Missing) 83419
92.4%
ValueCountFrequency (%)
44 1
 
< 0.1%
47 1
 
< 0.1%
49 4
< 0.1%
61 4
< 0.1%
63 3
< 0.1%
64 3
< 0.1%
66 1
 
< 0.1%
69 5
< 0.1%
79 6
< 0.1%
96 1
 
< 0.1%
ValueCountFrequency (%)
8352 1
< 0.1%
8195 1
< 0.1%
7625 1
< 0.1%
6906 1
< 0.1%
6870 1
< 0.1%
6805 1
< 0.1%
6615 1
< 0.1%
6572 1
< 0.1%
6465 1
< 0.1%
6271 1
< 0.1%

finishedsquarefeet6
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct360
Distinct (%)85.5%
Missing89854
Missing (%)99.5%
Infinite0
Infinite (%)0.0%
Mean2302.5463
Minimum257
Maximum7224
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2023-11-23T00:15:33.632152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum257
5-th percentile684
Q11112
median2028
Q33431
95-th percentile4553
Maximum7224
Range6967
Interquartile range (IQR)2319

Descriptive statistics

Standard deviation1346.2552
Coefficient of variation (CV)0.58468107
Kurtosis-0.30058561
Mean2302.5463
Median Absolute Deviation (MAD)1040
Skewness0.6633516
Sum969372
Variance1812403.2
MonotonicityNot monotonic
2023-11-23T00:15:33.708518image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720 5
 
< 0.1%
4442 4
 
< 0.1%
2180 3
 
< 0.1%
4345 3
 
< 0.1%
3500 3
 
< 0.1%
3470 3
 
< 0.1%
480 3
 
< 0.1%
2530 3
 
< 0.1%
572 3
 
< 0.1%
1000 2
 
< 0.1%
Other values (350) 389
 
0.4%
(Missing) 89854
99.5%
ValueCountFrequency (%)
257 1
 
< 0.1%
300 1
 
< 0.1%
360 1
 
< 0.1%
384 1
 
< 0.1%
438 1
 
< 0.1%
480 3
< 0.1%
520 1
 
< 0.1%
529 1
 
< 0.1%
531 1
 
< 0.1%
572 3
< 0.1%
ValueCountFrequency (%)
7224 1
< 0.1%
6338 1
< 0.1%
6337 1
< 0.1%
6265 1
< 0.1%
5300 1
< 0.1%
5287 2
< 0.1%
5268 1
< 0.1%
5229 1
< 0.1%
4996 1
< 0.1%
4967 2
< 0.1%

fips
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
6037.0
58574 
6059.0
24505 
6111.0
7196 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters541650
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6037.0
2nd row6059.0
3rd row6037.0
4th row6037.0
5th row6059.0

Common Values

ValueCountFrequency (%)
6037.0 58574
64.9%
6059.0 24505
27.1%
6111.0 7196
 
8.0%

Length

2023-11-23T00:15:33.776316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-23T00:15:33.822799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
6037.0 58574
64.9%
6059.0 24505
27.1%
6111.0 7196
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 173354
32.0%
6 90275
16.7%
. 90275
16.7%
3 58574
 
10.8%
7 58574
 
10.8%
5 24505
 
4.5%
9 24505
 
4.5%
1 21588
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 451375
83.3%
Other Punctuation 90275
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 173354
38.4%
6 90275
20.0%
3 58574
 
13.0%
7 58574
 
13.0%
5 24505
 
5.4%
9 24505
 
5.4%
1 21588
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 90275
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 541650
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 173354
32.0%
6 90275
16.7%
. 90275
16.7%
3 58574
 
10.8%
7 58574
 
10.8%
5 24505
 
4.5%
9 24505
 
4.5%
1 21588
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 541650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 173354
32.0%
6 90275
16.7%
. 90275
16.7%
3 58574
 
10.8%
7 58574
 
10.8%
5 24505
 
4.5%
9 24505
 
4.5%
1 21588
 
4.0%

Interactions

2023-11-23T00:15:29.629307image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:17.146963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:18.137626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:18.967675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:19.750406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:20.548901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:21.238288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:22.110617image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:23.057025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:23.859902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:24.710822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:25.534493image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:26.490482image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:27.372769image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:28.073471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:28.875314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:29.683001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:17.210600image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:18.193668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:19.023794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:19.800825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:20.593682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:21.298481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:22.170304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:23.113489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:23.920113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-11-23T00:15:25.592373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
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2023-11-23T00:15:18.867592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:19.667220image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:20.469068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:21.148386image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:22.005975image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:22.870974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:23.774945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:24.610087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:25.438494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:26.385067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:27.282582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:27.978618image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:28.793253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:29.544334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:30.319156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:18.085699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:18.917376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:19.707006image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:20.509248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:21.194718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:22.055675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:22.917114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:23.813845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:24.659820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:25.488904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:26.435277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:27.331915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:28.019430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:28.836908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-23T00:15:29.590906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-23T00:15:33.871488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
parcelidlogerrorairconditioningtypeidarchitecturalstyletypeidbasementsqftbathroomcntbedroomcntbuildingqualitytypeidcalculatedbathnbrfinishedfloor1squarefeetcalculatedfinishedsquarefeetfinishedsquarefeet12finishedsquarefeet13finishedsquarefeet15finishedsquarefeet50finishedsquarefeet6fips
parcelid1.0000.0280.4260.0270.281-0.0100.0020.134-0.0000.0780.0060.0210.246-0.0410.0730.1150.031
logerror0.0281.0000.0090.0450.0990.0670.042-0.0290.067-0.0130.0740.079-0.1220.040-0.014-0.0040.037
airconditioningtypeid0.4260.0091.000-0.155NaN-0.0450.0940.009-0.042NaN0.0890.090NaN0.008NaN-0.0740.822
architecturalstyletypeid0.0270.045-0.1551.000NaN0.0060.079NaN0.006NaN-0.040-0.040NaNNaNNaNNaN0.994
basementsqft0.2810.099NaNNaN1.0000.4090.588NaN0.3980.0970.2740.274NaNNaN-0.001NaN1.000
bathroomcnt-0.0100.067-0.0450.0060.4091.0000.582-0.4021.0000.2710.7440.7540.2530.7070.268-0.5150.093
bedroomcnt0.0020.0420.0940.0790.5880.5821.000-0.0740.5650.5010.7030.7110.2590.6810.496-0.5120.065
buildingqualitytypeid0.134-0.0290.009NaNNaN-0.402-0.0741.000-0.402NaN-0.317-0.371NaN-0.219NaNNaN1.000
calculatedbathnbr-0.0000.067-0.0420.0060.3981.0000.565-0.4021.0000.2760.7620.758-1.0000.7550.2730.6490.089
finishedfloor1squarefeet0.078-0.013NaNNaN0.0970.2710.501NaN0.2761.0000.6090.609NaN0.5000.995NaN1.000
calculatedfinishedsquarefeet0.0060.0740.089-0.0400.2740.7440.703-0.3170.7620.6091.0001.0001.0001.0000.6051.0000.046
finishedsquarefeet120.0210.0790.090-0.0400.2740.7540.711-0.3710.7580.6091.0001.000NaNNaN0.605NaN0.066
finishedsquarefeet130.246-0.122NaNNaNNaN0.2530.259NaN-1.000NaN1.000NaN1.000NaNNaNNaN1.000
finishedsquarefeet15-0.0410.0400.008NaNNaN0.7070.681-0.2190.7550.5001.000NaNNaN1.0000.500NaN0.087
finishedsquarefeet500.073-0.014NaNNaN-0.0010.2680.496NaN0.2730.9950.6050.605NaN0.5001.000NaN1.000
finishedsquarefeet60.115-0.004-0.074NaNNaN-0.515-0.512NaN0.649NaN1.000NaNNaNNaNNaN1.0001.000
fips0.0310.0370.8220.9941.0000.0930.0651.0000.0891.0000.0460.0661.0000.0871.0001.0001.000

Missing values

2023-11-23T00:15:30.412407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-23T00:15:30.615285image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-23T00:15:31.005016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

parcelidlogerrortransactiondateairconditioningtypeidarchitecturalstyletypeidbasementsqftbathroomcntbedroomcntbuildingclasstypeidbuildingqualitytypeidcalculatedbathnbrdecktypeidfinishedfloor1squarefeetcalculatedfinishedsquarefeetfinishedsquarefeet12finishedsquarefeet13finishedsquarefeet15finishedsquarefeet50finishedsquarefeet6fips
0110165940.02762016-01-011.0NaNNaN2.03.0NaN4.02.0NaNNaN1684.01684.0NaNNaNNaNNaN6037.0
114366692-0.16842016-01-01NaNNaNNaN3.54.0NaNNaN3.5NaNNaN2263.02263.0NaNNaNNaNNaN6059.0
212098116-0.00402016-01-011.0NaNNaN3.02.0NaN4.03.0NaNNaN2217.02217.0NaNNaNNaNNaN6037.0
3126434130.02182016-01-021.0NaNNaN2.02.0NaN4.02.0NaNNaN839.0839.0NaNNaNNaNNaN6037.0
414432541-0.00502016-01-02NaNNaNNaN2.54.0NaNNaN2.5NaNNaN2283.02283.0NaNNaNNaNNaN6059.0
511509835-0.27052016-01-021.0NaNNaN4.04.0NaN1.04.0NaNNaN3067.03067.0NaNNaNNaNNaN6037.0
6122860220.04402016-01-02NaNNaNNaN1.02.0NaN7.01.0NaNNaN1297.01297.0NaNNaNNaNNaN6037.0
7171773010.16382016-01-02NaNNaNNaN2.53.0NaNNaN2.5NaN853.01763.01763.0NaNNaN853.0NaN6111.0
814739064-0.00302016-01-02NaNNaNNaN1.02.0NaNNaN1.0NaNNaN796.0796.0NaNNaNNaNNaN6059.0
9146775590.08432016-01-03NaNNaNNaN2.02.0NaNNaN2.0NaNNaN1260.01260.0NaNNaNNaNNaN6059.0
parcelidlogerrortransactiondateairconditioningtypeidarchitecturalstyletypeidbasementsqftbathroomcntbedroomcntbuildingclasstypeidbuildingqualitytypeidcalculatedbathnbrdecktypeidfinishedfloor1squarefeetcalculatedfinishedsquarefeetfinishedsquarefeet12finishedsquarefeet13finishedsquarefeet15finishedsquarefeet50finishedsquarefeet6fips
9026512062080-0.37692016-12-301.0NaNNaN1.02.0NaN4.01.0NaNNaN860.0860.0NaNNaNNaNNaN6037.0
9026612265636-0.00302016-12-30NaNNaNNaN2.04.0NaN7.02.0NaNNaN1496.0NaNNaN1496.0NaNNaN6037.0
90267171096800.00102016-12-30NaNNaNNaN2.04.0NaNNaN2.0NaN1430.01830.01830.0NaNNaN1430.0NaN6111.0
9026812268527-0.01512016-12-301.0NaNNaN3.03.0NaN4.03.0NaNNaN2438.02438.0NaNNaNNaNNaN6037.0
90269129207460.03442016-12-30NaNNaNNaN2.03.0NaN4.02.0NaNNaN1448.01448.0NaNNaNNaNNaN6037.0
9027010774160-0.03562016-12-301.0NaNNaN1.01.0NaN4.01.0NaNNaN653.0653.0NaNNaNNaNNaN6037.0
90271120466950.00702016-12-30NaNNaNNaN3.03.0NaN4.03.0NaNNaN2856.02856.0NaNNaNNaNNaN6037.0
9027212995401-0.26792016-12-30NaNNaNNaN2.04.0NaN7.02.0NaNNaN2617.0NaNNaN2617.0NaNNaN6037.0
90273114021050.06022016-12-30NaNNaNNaN2.02.0NaN4.02.0NaNNaN1034.01034.0NaNNaNNaNNaN6037.0
90274125662930.42072016-12-30NaNNaNNaN1.03.0NaN7.01.0NaNNaN1524.01524.0NaNNaNNaNNaN6037.0